Identification and prediction using recurrent compensatory neuro-fuzzy systems

In this paper, a recurrent compensatory neuro-fuzzy system (RCNFS) for identification and prediction is proposed. The compensatory-based fuzzy method uses the adaptive fuzzy operations of neuro-fuzzy systems to make fuzzy logic systems more adaptive and effective. A recurrent network is embedded in the RCNFS by adding feedback connections in the second layer, where the feedback units act as memory elements. In this paper, the RCNFS model is proved to be a universal approximator. Also, an online learning algorithm is proposed which can automatically construct the RCNFS. There are no rules initially in the RCNFS. They are created and adapted as online learning proceeds through simultaneous structure and parameter learning. Structure learning is based on the degree measure and parameter learning is based on the ordered derivative algorithm. Finally, the RCNFS is used in several simulations. The simulation results of the dynamic system model have shown that (1) the RCNFS model converges quickly; (2) the RCNFS model requires a small number of tuning parameters; (3) the RCNFS model can solve temporal problems and approximate a dynamic system.